Need. In the US, 40 million patients with hypertension (HTN) have their blood pressure (BP) uncontrolled. BP above clinical Target even for a few months increases the risk for stroke (35-40%), heart failure (HF) (up to 64%), myocardial infarction (MI) (15-25%). Physician-nurse-pharmacist resource-intensive demonstrations in achieving & maintaining BP Target have shown promising results, but their real-life deployment was found unsustainable long-term. As a result, a process-standardized and sustainable solution is acutely needed. Solution. In response to this need, Optima Integrated Health developed optima4BP 1.0. It is a first-in-class artificial intelligence (AI) that simulates the process of clinical reasoning undertaken by the treating physician in optimizing the anti-HTN treatment towards BP Target. Just like the physician, optima4BP 1.0 cannot determine upfront the needed Optimal Treatment (OT) to achieve & maintain BP Target for 1-2 years. PROTECT [optima4BP 2.0: prediction of Optimal Treatment and route to achieve and maintain BP Target] proposes to establish upfront the personalized OT. The OT can then be used to select the shortest and safest treatment modification route needed to achieve & maintain BP Target. Phase II Goal. Build optima4BP 2.0. Phase I. Phase I Prior Work demonstrated that k-Nearest Neighbor (kNN), an AI model, can predict with ? 80% confidence the correct anti-HTN treatment, when compared to physician decision. Phase II. optima4BP 2.0 will predict the Optimal Treatment and route to achieve & maintain BP Target. Optimal Treatment data-mining source. PROTECT will use the SPRINT (Systolic Blood Pressure Intervention Trial, 2015) and ACCORD (Action to Control Cardiovascular Risk in Diabetes, 2010) clinical trial data. They represent the foundation of the most current anti-HTN treatment management national guidelines. Aim 1. Build kNN. Hypothesis. kNN can predict the proximity (clinical relevance) of a patient to an Optimal Treatment (OT). Milestone. Achieve ? 90% accuracy of prediction to physician decision. Phase I Data Preparation protocol will be applied to the SPRINT & ACCORD data. Then, the kNN Ensemble Learning function will be built to select the Optimal Treatment with the highest demonstrated efficacy by comparing the choice from 3 computational approaches developed and tested during Phase I. Aim 2. Build the Optimal Treatment Route (OTR). Hypothesis. Knowing the Current and Optimal Treatment (OT), an OTR can be built. Milestone. Safest Route: Achieve 100% exclusion of treatments that led to an adverse event in similar patient populations. Shortest Route: Achieve ?30% reduction in number of treatment changes compared to physician route. The OTR will be built by comparing at each Step on the Route how similar each Candidate Treatment is to the OT through a computed similarity assessment. optima4BP 2.0 aims to establish a process-standardized & sustainable solution with the goal of reducing the incidence of stroke, HF, MI and death resulting from uncontrolled hypertension.